The invention pertains to machine vision and, more particularly, to calibration targets and methods for determining their location and orientation in an image.
Machine vision refers to the automated analysis of an image to determine characteristics of objects and other features shown in the image. It is often employed in automated manufacturing lines, where images of components are analyzed to determine placement and alignment prior to assembly. Machine vision is also used for quality assurance. For example, in the pharmaceutical and food packing industries, images of packages are analyzed to insure that product labels, lot numbers, "freshness" dates, and the like, are properly positioned and legible.
In many machine vision applications, it is essential that an object whose image is to be analyzed include a calibration target. Often a cross-shaped symbol, the target facilitates determining the orientation and position of the object with respect to other features in the image. It also facilitates correlating coordinate positions in the image with those in the "real world," e.g., coordinate positions of a motion stage or conveyor belt on which the object is placed. A calibration target can also be used to facilitate determining the position and orientation of the camera with respect to the real world, as well as to facilitate determining the camera and lens parameters such as pixel size and lens distortion.
In addition to cross-shaped marks, the prior art suggests the use of arrays of dots, bulls-eyes of concentric circles, and parallel stripes as calibration targets. Many of these targets have characteristics that make difficult finding their centers and orientations. This typically results from lack of clarity when the targets and, particularly, their borders are imaged. It also results from discrepancies in conventional machine vision techniques used to analyze such images. For example, the edges of a cross-shaped target may be imprecisely defined in an image, leading a machine vision analysis system to wrongly interpret the location of those edges and, hence, to misjudge the mark's center by a fraction of a pixel or more. By way of further example, a localized defect in a camera lens may cause a circular calibration mark to appear as an oval, thereby, causing the system to misjudge the image's true aspect ratio.
In addition to the foregoing, many of the prior art calibration targets are useful only at a limited range of magnifications. Parallel stripes, for example, do not provide sufficient calibration information unless many of them appear in an image. To accommodate this, a machine vision system must utilize lower magnification. However, as the magnification decreases, so does the ability of the machine vision equipment to distinguish between individual stripes. Similar drawbacks limit the usefulness of the other prior art calibration targets for use in all but a narrow range of magnifications.
Though the art suggests the use of checkerboard patterns as alignment marks, the manner in which images of those marks are analyzed by conventional machine systems also limits their utility to a limited range of magnifications. Particularly, prior art systems obtain alignment information from checkerboard marks by identifying and checking their corners, e.g., the eight black (or white) corners in a black-and-white image. By relying on corners, the systems necessitate that images show entire checkerboards, yet, with sufficient resolution to insure accurate detection and analysis.
An object of this invention is to provide an improved calibration targets and methods for machine vision analysis thereof.
A related object is to provide calibration targets and analysis methods reliable at a wide range of magnifications.
A further object is to provide such methods as can be readily implemented on conventional digital data processors or other conventional machine vision analysis equipment.
Yet still another object of the invention is to provide such methods that can rapidly analyze images of calibration target without undue consumption of resources.